Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms

DOI Web Site Web Site 被引用文献1件 参考文献23件
  • Ye Chen
    Department of Electronics and Information Systems, Akita Prefectural University
  • Gui Guan
    College of Telecommunication and Information Engineering, Nanjing University of Post and Telecommunications
  • Matsushita Shin-ya
    Department of Electronics and Information Systems, Akita Prefectural University
  • Xu Li
    Department of Electronics and Information Systems, Akita Prefectural University

この論文をさがす

抄録

<p>Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “block ℓ0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.</p>

収録刊行物

被引用文献 (1)*注記

もっと見る

参考文献 (23)*注記

もっと見る

関連プロジェクト

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ